What are some strategies for AI professionals to improve their project management skills?

In the early days of SensViz, clarity on project objectives was crucial. I made sure to understand the problem thoroughly before diving into solutions. It was not just about identifying the issues, but also understanding the value solving them would bring. This approach helped in aligning the team and stakeholders. We knew what we were aiming for, and why it was important. It made discussions productive and kept us on track, saving time and resources. This simple yet effective strategy significantly sharpened my project management skills. It instilled a habit of seeking clarity which proved invaluable in guiding SensViz projects to success.

I think that a project management certification can be really helpful on knowing where to begin. I’ve been studying for Agile and PMP have found the training intensely helpful. Basically any AI development is going to be Agile based.

Consider the process of generating A.I. technologies as engineering or a business process with the following steps. For training data, how are you going to collect and "clean/prepare" your training data. What are the requirements around this? Lots of data? Temporal nature of the data or sources themselves? IP rights? For the training process, where are you going to get a bunch of GPUs? Cloud-based, a university, Cloud credits, roll your own? This is a hardware engineering and DevOps issue. Then, what framework: TensorFlow or Torch? Or some higher level API on top, like Fast.AI? Then for inference, are you going to expose your A.I. as a microservice? What are the cyber security constraints, etc. What are the end users and performance?

As the VP at Adroit Vantage, I understand the critical role project management plays in the success of our AI initiatives in Wealth-Tech. To improve the project management skill for an AI project, we emphasise the following strategies: * We begin every AI project by clearly defining the problem we aim to solve. This involves extensive collaboration with business stakeholders to understand their pain points and expectations. * Identifying the value proposition is equally important. We work closely with CIO team, RMs and end-users to ascertain how the AI project will enhance their experience or improve business operations. * We adopt an iterative project management approach to remain agile and responsive to evolving requirements.

When defining the problem for an AI project, it's crucial to engage in customer discovery sessions, not only to ensure understanding of their needs but also to unearth latent needs that customers may not explicitly express. Coupled with the value proposition, consider applying design thinking methodologies, which place the end-user at the center of the problem-solving equation. This can lead to innovative solutions that not only meet but exceed user expectations, creating a competitive edge and ensuring that the AI project is not just a technological showcase but a valuable tool in addressing real-world problems.

Selecting the right methodology and tools is pivotal for managing AI projects efficiently. For instance, switching to Agile from Waterfall can foster faster feedback and adjustments, crucial in AI's fast-paced domain. Additionally, choosing tools that align with the team's skills and project needs can streamline workflows, enhancing collaboration and tracking. These choices, tailored to the project and team's demands, can significantly impact the project's success, making it essential to choose wisely based on project specifics.

Tools like Jira, Monday, Asana Trello, etc, are making project managers' lives much easier, including AI projects. In my opinion, practice experience matters the most, and every team, situation, and project nature is different and you have to utilize the flexibility of Agile and tools to tailor your execution accordingly. I am certified in CSM, PMP, and SAFe, covering Agile methodologies, comprehensive project management, and scaled Agile for large projects. Certifications help you to know the process but it's your experience that helps you to deliver. My favorite AI-embedded tools for automation include JIRA automation, Slack bots, Scrum Assistant bot for auto scrum ceremonies, and Grammarly to summarize and respond to emails.

Gratefully, there are a various project management frameworks that can be applied according to the need of the project. My favorite is SAFe: as an agile framework, it can be adopted to the entire organization and concentrates important elements for AI projects: - Iterations and getting into a flow are at the center of development - leading roles are targeted to be enabling, facilitating and coaching team members (as opposed to the classical project management roles) - becoming a learning organization is a key aspect when delivering within this framework

Begin by aligning your project's nature with a suitable methodology, whether Agile, Scrum, or a hybrid approach. Leverage project management tools like Jira, Trello, or Asana to streamline tasks, foster collaboration, and track progress. Incorporate version control systems like Git to manage code changes effectively. Embrace AI-specific tools such as TensorFlow or PyTorch for seamless integration with project workflows. This strategic combination ensures efficient project delivery, adaptability to evolving requirements, and optimal utilization of AI capabilities in project management.

AI projects are iterative in nature and waterfall model may not align with it. Kanban/Scrum offer the necessary flexibility and adaptability required for AI projects. As AI professional, it's good to build knowledge about Agile best practices, such as iterative development, continuous integration, feedback mechanisms, and Test-Driven Development (TDD)

In my experience effective data and model management is the backbone of successful AI projects. Beyond technical steps, it demands a holistic approach. It necessitates not only accuracy but also data ethics, as biased data can lead to biased models. Regularly reassess your data and models to adapt to evolving real-world conditions and ensure they align with your project's goals. Ethical AI should be at the core of your strategy, and explainability is essential for transparency and trust. In an ever-changing landscape, continuous monitoring and adaptability are key to AI success.

The cornerstone of AI projects is the adept management of data and models. Ensuring data quality and model integrity is non-negotiable. For me, data isn't just data—it's the foundation upon which reliable and ethical AI is built. Adopting a comprehensive data governance framework is critical, especially one that respects privacy and utility. When it comes to models, robustness and fairness are paramount. I advocate for an approach where models are not only trained to perform but also to be understood and accountable. Techniques that foster resilience to novel data inputs, coupled with explainability practices, are integral. It’s about creating AI solutions that are not only powerful but also principled and transparent.

?? Managing Data and Models: Data Quality: ?? Ensure clean, reliable datasets. Version Control: ?? Track changes in code and model versions. Security: ?? Implement robust data and model security measures. Documentation: ?? Thoroughly document data sources and model architectures. Monitoring: ?? Regularly assess model performance and data quality. ?? Overall: Balancing project management acumen with robust data and model management ensures successful AI endeavors!

In the AI world, data and models are the heroes. Good data is vital, and we clean it up and make it better. Models must be smart and ethical. We use techniques to ensure they work well. Taking care of data and models lets AI solve real problems and make a positive impact.

Data!data!data!" he cried impatiently. "I can't make bricks without clay. (Arthur Conan Doyle, The Adventure of the Copper Beeches - a Sherlock Holmes Short Story, 1892) Since then we should all know that data is key to any successful project!

Ever wondered what drives the AI project success? The answer lies in resource allocation – a strategic art. It's not just about meeting project goals; it's about optimizing them. Prioritizing tasks, managing time, and budgeting effectively are the essentials that may not be the focusing key factor of the project. As AI progressively grows every minute, adaptability doesn't become only a choice but a necessity. Shifting resources in response to evolving needs and unforeseen challenges is the secret sauce. I would recommend directing your focus towards strategic resource allocation, the compass guiding you to smoother AI project journeys. Embrace punctual milestones, and navigate AI projects confidently into the future of technology.

Communication across stakeholders is key to success of AI project. Successful deployment of AI projects often requires becoming acquainted with unfamiliar knowledge and collaborating with other professions

Communication is not about the channel. The channel or tool is just one of the component of communications. Instead, focus on who is your stakeholder. What is their preferences? What information are they looking for? What is the cadence of communication? How do they prefer to consume the information? Once these questions are clarified, craft a communication strategy that fit the needs of your stakeholders. Bonus tip, consider the generation to which each stakeholder belongs to. I have found a new concept extremely popular among Z-gens coined as fluid communication. It relies on composing project-update in a ‘twit’-format or ‘tik-tok’-video with a high cadence. This is just an example how the receiver conditions the nature of the message.

?? Effective Communication Tips: Clarity ??: Clearly articulate goals and expectations. Active Listening ??:Understand team concerns and feedback Regular Updates ??: Keep stakeholders informed of progress Document Everything ??: Maintain a record of decisions and discussions Feedback Culture ??: Encourage open dialogue for improvement ?? Transparency Tactics: Honesty ??: Be upfront about challenges&successes Accessibility ??: Ensure information is easily accessible Feedback Loops ??: Establish mechanisms for continuous feedback Collaborative Tools ???: Use platforms that enhance transparency Celebrate Wins ??: Acknowledge achievements to boost morale By integrating these strategies and communication tips,AI professionals can enhance skills

Effective communication in AI projects goes beyond just updating stakeholders. It involves demystifying AI for non-technical stakeholders. In my projects, I often use analogies and simple visualizations to explain complex AI concepts, which helps in building trust and alignment. Develop your ability to explain AI concepts in simple terms. Regularly check for understanding, especially with stakeholders who might not have a technical background.

Achieving success with AI projects can be challenging and requires a significant amount of data. Despite having a large data science team, I witnessed one of the largest job portal websites struggle for over a year. While with Generative AI, they suddenly achieved exceptional results and ROI within weeks. The key takeaway here is that it's not always your fault when a project fails. There could be various factors such as environmental challenges, organizational challenges, stakeholder challenges, lack of data, computation power, or absence of innovative solutions. Achieving success requires consistency and periodic retrospectives to learn from your mistakes, re-evaluate, think back, and come back stronger with renewed energy and vision.

This is why I recommend using an agile methodology to deliver your AI projects. You need to have a retrospective after each sprint and adjust accordingly. You need the flexibility to fail fast and recover.

The journey of managing AI projects is akin to navigating a labyrinth of constant evolution, where the path to mastery is paved with the bricks of both failures and successes. It demands an unflinching commitment to self-evaluation, a readiness to confront one's own shortcomings, and the agility to adapt and evolve. Embracing tools such as retrospectives and post-mortems transforms setbacks into stepping stones, turning every project, irrespective of its outcome, into a valuable lesson. The true art, however, lies in not just internalizing these lessons but boldly broadcasting them through channels like surveys and blogs, contributing to a communal pool of wisdom.

Measuring success/failure in an AI project involves assessing two key metrics: outcome metrics and output metrics. Outcome metrics reflect the business value generated by the model, and output metrics assess the technical performance of the mode.

Mastering project management is all about your mindset and relentless commitment to learning the ropes. From the project's very inception, envision how it will flex and adapt with expanding data, users, and evolving features. With this forward-thinking approach, you'll sidestep potential roadblocks and resource constraints that could otherwise slow your progress. It's not just planning; it's about ensuring your AI solution effortlessly meets the ever-changing demands of a dynamic landscape. It's your key to sustained excellence and unlocking AI's full potential. Remember that scalability isn't just a feature; it's a strategy that transforms the ordinary into the extraordinary, setting your AI projects on a path to continuous innovation!

Additional traits of exceptional AI PMs include: - Risk Management: They anticipate and plan for potential risks, including ethical considerations of AI use, ensuring AI is developed and used responsibly. - Innovation: They recognize that AI development requires a degree of trial and error, and foster an environment that encourages creative thinking and experimentation. - Stakeholder Management: AI projects require collaboration with more diverse groups than traditional IT projects. Great AI PMs identify all the stakeholder groups early, including end users, business teams, data providers, domain experts, execs, legal, etc. And they proactively prioritize the right groups to engage throughout the project lifecycle.

In the journey of managing AI projects, continuous learning and adapting are the keys. I recall when we initiated SensViz, every project brought a new learning curve. Over time, cultivating a culture of open communication within the team and with stakeholders significantly optimized our project management processes. Also, staying updated with the latest project management tools and AI technologies helped in aligning our strategies effectively with the evolving demands. Lastly, embedding ethics in every project phase ensured that our AI solutions were not only technologically sound but also socially responsible.

To improve your project management skills, you should add value to the project. Projects that are well executed and satisfy the business are often seen as well managed. As an AI professional, think how you can use AI to optimize the project components and make them more cost-effective. The best strategy or approach for a successful project is to focus on the minimum viable products (MVPs), identify the pain points, and address them. Don't spend too much time on what else could be added, but rather think how the existing solution can be improved with AI. Remember, being fit is better than having a six-pack abs. These are small but important steps.

In the dynamic world of Artificial Intelligence, change is a cornerstone. An effective team player in this realm has to be a maestro of adaptation, being able to navigate through a symphony of transformation. At the heart of mastering this art is the mindset that change is a constant companion, not a stumbling block. Also an AI project comprises of convergence of diverse experts, pooling their talents to orchestrate technological marvels. This necessitates a harmonious collaboration that extends beyond individual brilliance. Herein lies the crux of key capability required by AI professionals - capability to work together in a multidisciplinary team as a cohesive unit that amplifies each member’s expertise.

‘Never say Never’? I would still take the liberty and suggest: Never underestimate your current knowledge and expertise when starting something unknown or new! With this mindset, I turn to my preferred method of visualizing an outcome and starting from where I am. Reverse Engineering has been my truest ally for decades. Envision where you believe you should be in ten years, months, or even days. Then, reverse engineer your path. The trajectory will shift, making the beginning uniquely yours - a 'constant' for at least a few more minutes while you read and reread this comment. Your outcome will depend on the strategy you choose or create!